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Mid-level GenAI role in metro with hybrid work and recognizable employer increases applicant competition.
GenAI and LLM engineering skills transfer across industries but require ML-specific expertise.
Explicit 6-8 years plus mandatory GenAI, LLM, and deployment skills make filters highly strict.
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Develop and deploy Generative AI applications using LLMs like GPT, Gemini, and LLaMA, integrating AI capabilities into enterprise applications via APIs and cloud-native architectures.
Build and manage multi-agent AI systems and implement retrieval-augmented generation (RAG) pipelines using vector databases such as FAISS, Qdrant, Weaviate, and ChromaDB.
Collaborate with stakeholders to deliver AI-driven business solutions and provide analysis, insights, and recommendations based on Generative AI technologies.
Bachelor's degree in Computer Science, Engineering, Statistics, or related technical field.
6 - 8 years of experience in Data Analytics, Data Science, or Generative AI roles with proven delivery of data-driven AI solutions in production environments.
Proficiency in Python, PySpark, SQL, and data science libraries including Pandas, NumPy, Scikit-learn, and Matplotlib.
Experience with GenAI platforms/tools (OpenAI, Anthropic, Google Vertex AI), AI frameworks (FastAPI, Gradio, Streamlit), and knowledge of RAG and Agentic AI architectures using LangChain and Lang Graph.
Experienced senior data scientist or AI specialist with hands-on expertise in Generative AI and multi-agent system development.
Strong technical knowledge in deploying AI solutions at scale using cloud-native architectures and API-driven integrations.
Ability to translate complex AI frameworks and models into practical business solutions through collaboration with diverse stakeholders.